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1,060
Towards an axiomatization of statistical privacy and utility
- In PODS
, 2010
"... “Privacy ” and “utility ” are words that frequently appear in the literature on statistical privacy. But what do these words really mean? In recent years, many problems with intuitive notions of privacy and utility have been uncovered. Thus more formal notions of privacy and utility, which are amena ..."
Abstract
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Cited by 25 (10 self)
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“Privacy ” and “utility ” are words that frequently appear in the literature on statistical privacy. But what do these words really mean? In recent years, many problems with intuitive notions of privacy and utility have been uncovered. Thus more formal notions of privacy and utility, which
AN AXIOMATIC VIEW OF STATISTICAL PRIVACY AND UTILITY
"... Abstract. “Privacy ” and “utility ” are words that frequently appear in the literature on statistical privacy. But what do these words really mean? In recent years, many problems with intuitive notions of privacy and utility have been uncovered. Thus more formal notions of privacy and utility, which ..."
Abstract
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Cited by 12 (6 self)
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Abstract. “Privacy ” and “utility ” are words that frequently appear in the literature on statistical privacy. But what do these words really mean? In recent years, many problems with intuitive notions of privacy and utility have been uncovered. Thus more formal notions of privacy and utility
Calibrating noise to sensitivity in private data analysis
- In Proceedings of the 3rd Theory of Cryptography Conference
, 2006
"... Abstract. We continue a line of research initiated in [10, 11] on privacypreserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function f mapping databases to reals, the so-called true answer is the result of applying f to the datab ..."
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Cited by 649 (60 self)
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obtain separation results showing the increased value of interactive sanitization mechanisms over non-interactive. 1 Introduction We continue a line of research initiated in [10, 11] on privacy in statistical data-bases. A statistic is a quantity computed from a sample. Intuitively, if the database is a
Data Security
, 1979
"... The rising abuse of computers and increasing threat to personal privacy through data banks have stimulated much interest m the techmcal safeguards for data. There are four kinds of safeguards, each related to but distract from the others. Access controls regulate which users may enter the system and ..."
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Cited by 615 (3 self)
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The rising abuse of computers and increasing threat to personal privacy through data banks have stimulated much interest m the techmcal safeguards for data. There are four kinds of safeguards, each related to but distract from the others. Access controls regulate which users may enter the system
Information Preservation in Statistical Privacy and Bayesian Estimation of Unattributed Histograms
"... In statistical privacy, utility refers to two concepts: information preservation – how much statistical information is retained by a sanitizing algorithm, and usability – how (and with how much difficulty) does one extract this information to build statistical models, answer queries, etc. Some scena ..."
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Cited by 1 (0 self)
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In statistical privacy, utility refers to two concepts: information preservation – how much statistical information is retained by a sanitizing algorithm, and usability – how (and with how much difficulty) does one extract this information to build statistical models, answer queries, etc. Some
Revealing information while preserving privacy
- In PODS
, 2003
"... We examine the tradeoff between privacy and usability of statistical databases. We model a statistical database by an n-bit string d1,.., dn, with a query being a subset q ⊆ [n] to be answered by � i∈q di. Our main result is a polynomial reconstruction algorithm of data from noisy (perturbed) subset ..."
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Cited by 272 (9 self)
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We examine the tradeoff between privacy and usability of statistical databases. We model a statistical database by an n-bit string d1,.., dn, with a query being a subset q ⊆ [n] to be answered by � i∈q di. Our main result is a polynomial reconstruction algorithm of data from noisy (perturbed
Mitigating Storage Side Channels Using Statistical Privacy Mechanisms
"... A storage side channel occurs when an adversary accesses data objects influenced by another, victim computation and infers information about the victim that it is not permitted to learn directly. We bring advances in privacy for statisti-cal databases to bear on storage side-channel defense, and spe ..."
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A storage side channel occurs when an adversary accesses data objects influenced by another, victim computation and infers information about the victim that it is not permitted to learn directly. We bring advances in privacy for statisti-cal databases to bear on storage side-channel defense
Protecting Privacy when Disclosing Information: k-Anonymity and Its Enforcement through Generalization and Suppression
, 1998
"... Today's globally networked society places great demand on the dissemination and sharing of person-specific data. Situations where aggregate statistical information was once the reporting norm now rely heavily on the transfer of microscopically detailed transaction and encounter information. Thi ..."
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Cited by 315 (1 self)
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Today's globally networked society places great demand on the dissemination and sharing of person-specific data. Situations where aggregate statistical information was once the reporting norm now rely heavily on the transfer of microscopically detailed transaction and encounter information
Practical privacy: the sulq framework
- In PODS ’05: Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
, 2005
"... We consider a statistical database in which a trusted administrator introduces noise to the query responses with the goal of maintaining privacy of individual database entries. In such a database, a query consists of a pair (S, f) where S is a set of rows in the database and f is a function mapping ..."
Abstract
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Cited by 223 (35 self)
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We consider a statistical database in which a trusted administrator introduces noise to the query responses with the goal of maintaining privacy of individual database entries. In such a database, a query consists of a pair (S, f) where S is a set of rows in the database and f is a function mapping
Collaborative filtering with privacy via factor analysis
- In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
, 2002
"... Collaborative filtering is valuable in e-commerce, and for direct recommendations for music, movies, news etc. But today’s systems use centralized databases and have several disadvantages, including privacy risks. As we move toward ubiquitous computing, there is a great potential for individuals to ..."
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Cited by 210 (9 self)
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data. Our experiments on several test datasets show that the algorithm is more accurate than previously reported methods, and the improvements increase with the sparseness of the dataset. Finally, factor analysis with privacy is applicable to other kinds of statistical analyses of survey
Results 1 - 10
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